However, they are far from containing only interesting semantic relations .

cerns the type of semantic relations : results with Moby as reference are improved in a larger extent than results with WordNet as reference.

This suggests that our procedure is more effective for semantically related words than for semantically similar words, which can be considered as a little bit surprising since the notion of context in our discriminative classifier seems a priori more strict than in “classical” distributional contexts.

The term semantic neighbor is very generic and can have two main interpretations according to the kind of semantic relations it is based on: one relies only on paradigmatic relations, such as hy-pernymy or synonymy, while the other consid-

The distinction between these two interpretations refers to the distinction between the notions of semantic similarity and semantic relatedness as it was done in (Budanitsky and Hirst, 2006) or in (Zesch and Gurevych, 2010) for instance.

However, the limit between these two notions is sometimes hard to find in existing work as terms semantic similarity and semantic relatedness are often used interchangeably.

The building of distributional thesaurus is generally viewed as an application or a mode of evaluation of work about semantic similarity or semantic relatedness .

arises from the imbalance between semantic similarity and semantic relatedness among training examples: most of selected examples were pairs of words linked by semantic relatedness because this kind of relations are more frequent among semantic neighbors than relations based on semantic similarity.

The English ’5 possessive construction occurs frequently in text and can encode several different semantic relations ; however, it has received limited attention from the computational linguistics community.

This paper describes the creation of a semantic relation inventory covering the use of ’s, an inter-annotator agreement study to calculate how well humans can agree on the relations, a large collection of possessives annotated according to the relations, and an accurate automatic annotation system for labeling new examples.

Badulescu and Moldovan (2009) investigate both ’s-constructions and 0f constructions in the same context using a list of 36 semantic relations (including OTHER).

For the 960 extracted ’s—possessive examples, only 20 of their semantic relations are observed, including OTHER, with 8 of the observed relations occurring fewer than 10 times.

Also, it is sometimes difficult to understand the meaning of the semantic relations , partly because most relations are only described by a single example and, to a lesser extent, because the bulk of the given examples are of-constructions.

The English ’5 possessive construction occurs frequently in text—approximately 1.8 times for every 100 hundred words in the Penn Treebank1 (Marcus et al., l993)—and can encode a number of different semantic relations including ownership (John ’5 car), part-of-whole (John ’5 arm), extent (6 hours’ drive), and location (America ’s rivers).

These interpretations could be valuable for machine translation to or from languages that allow different semantic relations to be encoded by

This paper presents an inventory of 17 semantic relations expressed by the English ’s—construction, a large dataset annotated according to the this inventory, and an accurate automatic classification system.

The initial semantic relation inventory for possessives was created by first examining some of the relevant literature on possessives, including work by Badulescu and Moldovan (2009), Barker (1995), Quirk et al.

Table 2: The semantic relations proposed by Quirk et al.

Concerning the extension of the capabilities of BRAINSUP, we want to include commonsense knowledge and reasoning to profit from more sophisticated semantic relations and to inject humor on demand.

[YesMo]; 3) Relatedness: is the sentence semantically related to the target domain?

In other cases, such as “A sixth calorie may taste an own good” or “A same sunshine is fewer than a juice of day”, more sophisticated reasoning about syntactic and semantic relations in the output might be necessary in order to enforce the generation of sound and grammatical sentences.

(2011) slant existing textual expressions to obtain more positively or negatively valenced versions using WordNet (Miller, 1995) semantic relations and SentiWordNet (Esuli and Sebastiani, 2006) annotations.

Stock and Strapparava (2006) generate acronyms based on lexical substitution via semantic field opposition, rhyme, rythm and semantic relations .

(2012) attempt to generate novel poems by replacing words in existing poetry with morphologically compatible words that are semantically related to a target domain.

In this paper, a global argument inference model is proposed to infer those inter-sentence arguments and their roles, incorporating with semantic relations between relevant event mention pairs and argument semantics.

Therefore, employing those high level information to capture the semantic relation , not only the syntactic structure, between the trigger and its long distance arguments is the key to improve the performance of the Chinese argument identification.

Hence, the semantic relations among event mentions are helpful to be a bridge to identify those inter-sentence arguments.

1) We propose a novel global argument inference model, in which various kinds of event relations are involved to infer more arguments on their semantic relations .

In particular, dependency edges are often indicative of semantic relations .

To combat this problem, GUSP introduces a novel dependency-based meaning representation with an augmented state space to account for semantic relations that are nonlocal in the dependency tree.

GUSP only creates edge states for relational join paths up to length four, as longer paths rarely correspond to meaningful semantic relations .

by augmenting the state space to represent semantic relations beyond immediate dependency neighborhood.

With this approach, using a stop list does not have a major effect on results for most relation classes, which suggests most of the word pairs affecting performance are content word pairs which may truly be semantically related to the discourse structure.

The intuition is that these pairs will tend to represent semantic relationships which are related to the discourse marker (for example, word pairs often appearing around but may tend to be antonyms).

We show that our formulation outperforms the original one while requiring less features, and that using a stop list of functional words does not significantly affect performance, suggesting that these features indeed represent semantically related content word pairs.

These approaches are motivated by the idea that sentence meaning can be flexibly captured by the syntactic and semantic relations between words, and encoded in dependency parse tree fragments.

and ‘level play’ do not have an important semantic relationship relative to the query, yet these catenae are described by parent-child relations that are commonly used to filter paths in text processing applications.

This is based on the observation that semantically related words have a variety of direct and indirect relations.

Relation Extraction (RE) is the task of extracting semantic relationships between entities in text.

Relation extraction is the task of extracting semantic relationships between entities in text, e.g.

For instance, the fragments corresponding to governor from Texas and head of Maryland are intuitively semantically related and should obtain a higher match when compared to mother of them.

To strike a balance between these two extremes, we introduce a novel model of semantic coherence that is based on a measure of semantic relatedness between pairs of words.

We model semantic relatedness between two words using the Information Content (IC) of the pair in a method similar to the one used by Lin (1997) and Lin (1998).

During testing, for each phrase in our test set, we measure semantic relatedness of pairs of words using the IC values estimated from the Arabic Gigaword, and normalize their sum by the number of pairs in the phrase to obtain a measure of Semantic Coherence (SC) of the phrase.